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The time delay prompt procedures are different from SLP and MTL procedures because instead of removing prompts by progressing through a hierarchy, prompts are removed by delaying them in time. The progressive time delay procedure was developed first, [ 12 ] and the constant time delay procedure was developed as a more parsimonious procedure for ...
When a next-line predictor points to aligned groups of 2, 4, or 8 instructions, the branch target will usually not be the first instruction fetched, and so the initial instructions fetched are wasted. Assuming for simplicity, a uniform distribution of branch targets, 0.5, 1.5, and 3.5 instructions fetched are discarded, respectively.
Busy-waiting itself can be made much less wasteful by using a delay function (e.g., sleep()) found in most operating systems. This puts a thread to sleep for a specified time, during which the thread will waste no CPU time. If the loop is checking something simple then it will spend most of its time asleep and will waste very little CPU time.
JIT causes a slight to noticeable delay in the initial execution of an application, due to the time taken to load and compile the input code. Sometimes this delay is called "startup time delay" or "warm-up time". In general, the more optimization JIT performs, the better the code it will generate, but the initial delay will also increase.
In telecommunications, receive-after-transmit time delay is the time interval between (a) the instant of keying off the local transmitter to stop transmitting and (b) the instant the local receiver output has increased to 90% of its steady-state value in response to an RF signal from another transmitter.
A digital delay generator (also known as digital-to-time converter) is a piece of electronic test equipment that provides precise delays for triggering, syncing, delaying, and gating events. These generators are used in many experiments, controls, and processes where electronic timing of a single event or multiple events to a standard timing ...
This expression for T(p) is identical to the one derived earlier, for a sixth order delay, by the continued fraction method. A similar procedure can be used to determine the transfer functions of networks of all orders, that have a maximally flat time delay, although the procedure does become tedious for the higher orders.
The reward model is first trained in a supervised manner to predict if a response to a given prompt is good (high reward) or bad (low reward) based on ranking data collected from human annotators. This model then serves as a reward function to improve an agent's policy through an optimization algorithm like proximal policy optimization. [3] [4] [5]